title: ToPS: a framework to manipulate probabilistic models of sequence data.
abstract: Discrete Markovian models can be used to characterize patterns in sequences of values and have many applications in biological sequence analysis, including gene prediction, CpG island detection, alignment, and protein profiling. We present ToPS, a computational framework that can be used to implement different applications in bioinformatics analysis by combining eight kinds of models: (i) independent and identically distributed process; (ii) variable-length Markov chain; (iii) inhomogeneous Markov chain; (iv) hidden Markov model; (v) profile hidden Markov model; (vi) pair hidden Markov model; (vii) generalized hidden Markov model; and (viii) similarity based sequence weighting. The framework includes functionality for training, simulation and decoding of the models. Additionally, it provides two methods to help parameter setting: Akaike and Bayesian information criteria (AIC and BIC). The models can be used stand-alone, combined in Bayesian classifiers, or included in more complex, multi-model, probabilistic architectures using GHMMs. In particular the framework provides a novel, flexible, implementation of decoding in GHMMs that detects when the architecture can be traversed efficiently.
authors.affiliation: Graduate Program in Informatics, Federal University of Technology - Paraná, Cornélio Procópio, Paraná, Brazil.
authors.forename: André Yoshiaki
authors.initials: AY
authors.lastname: Kashiwabara
authors.forename: Igor
authors.initials: I
authors.lastname: Bonadio
authors.forename: Vitor
authors.initials: V
authors.lastname: Onuchic
authors.forename: Felipe
authors.initials: F
authors.lastname: Amado
authors.forename: Rafael
authors.initials: R
authors.lastname: Mathias
authors.forename: Alan Mitchell
authors.initials: AM
authors.lastname: Durham
mesh_terms: D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D018899:CpG Islands / Q000235:genetics; D008390:Markov Chains; D017421:Sequence Analysis / Q000379:methods*